docs: def load(self, label_lookup_path, uid_lookup_path):
      """Loads a human readable English name for each softmax node.
  
      Args:
        label_lookup_path: string UID to integer node ID.
        uid_lookup_path: string UID to human-readable string.
  
      Returns:
        dict from integer node ID to human-readable string.
      """
      if not tf.gfile.Exists(uid_lookup_path):
        tf.logging.fatal('File does not exist %s', uid_lookup_path)
      if not tf.gfile.Exists(label_lookup_path):
        tf.logging.fatal('File does not exist %s', label_lookup_path)
  
      # Loads mapping from string UID to human-readable string
      proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
      uid_to_human = {}
      p = re.compile(r'[n\d]*[ \S,]*')
      for line in proto_as_ascii_lines:
        parsed_items = p.findall(line)
        uid = parsed_items[0]
        human_string = parsed_items[2]
        uid_to_human[uid] = human_string
  
      # Loads mapping from string UID to integer node ID.
      node_id_to_uid = {}
      proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
      for line in proto_as_ascii:
        if line.startswith('  target_class:'):
          target_class = int(line.split(': ')[1])
        if line.startswith('  target_class_string:'):
          target_class_string = line.split(': ')[1]
          node_id_to_uid[target_class] = target_class_string[1:-2]
  
      # Loads the final mapping of integer node ID to human-readable string
      node_id_to_name = {}
      for key, val in node_id_to_uid.items():
        if val not in uid_to_human:
          tf.logging.fatal('Failed to locate: %s', val)
        name = uid_to_human[val]
        node_id_to_name[key] = name
  
      return node_id_to_name